{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xqLjB2cy5S7m"
   },
   "source": [
    "## MNIST in Keras with Tensorboard\n",
    "\n",
    "This sample trains an \"MNIST\" handwritten digit \n",
    "recognition model on a GPU or TPU backend using a Keras\n",
    "model. Data are handled using the tf.data.Datset API. This is\n",
    "a very simple sample provided for educational purposes. Do\n",
    "not expect outstanding TPU performance on a dataset as\n",
    "small as MNIST."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lvo0t7XVIkWZ"
   },
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cCpkS9C_H7Tl"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "LEARNING_RATE = 0.002\n",
    "# GCS bucket for training logs and for saving the trained model\n",
    "# You can leave this empty for local saving, unless you are using a TPU.\n",
    "# TPUs do not have access to your local instance and can only write to GCS.\n",
    "BUCKET=\"gs://ml1-demo-martin/mnist\" # a valid bucket name must start with gs://\n",
    "\n",
    "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
    "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
    "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
    "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qpiJj8ym0v0-"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "AoilhmYe1b5t",
    "outputId": "914f12e4-ca4e-4b92-ddf5-acdf57c0b13b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version 2.2.0-dlenv\n"
     ]
    }
   ],
   "source": [
    "import os, re, math, json, time\n",
    "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.python.platform import tf_logging\n",
    "print(\"Tensorflow version \" + tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPU/GPU detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Initializing the TPU system: martin-tpuv3-8-tf22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Initializing the TPU system: martin-tpuv3-8-tf22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Clearing out eager caches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Clearing out eager caches\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished initializing TPU system.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Finished initializing TPU system.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Found TPU system:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Found TPU system:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Cores: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Cores: 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Workers: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Workers: 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of accelerators:  8\n"
     ]
    }
   ],
   "source": [
    "try: # detect TPUs\n",
    "  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n",
    "  tf.config.experimental_connect_to_cluster(tpu)\n",
    "  tf.tpu.experimental.initialize_tpu_system(tpu)\n",
    "  strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
    "except ValueError: # detect GPUs\n",
    "  strategy = tf.distribute.MirroredStrategy() # for GPU or multi-GPU machines\n",
    "  #strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
    "  #strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy() # for clusters of multi-GPU machines\n",
    "\n",
    "print(\"Number of accelerators: \", strategy.num_replicas_in_sync)\n",
    "    \n",
    "# adjust batch size and learning rate for distributed computing\n",
    "global_batch_size = BATCH_SIZE * strategy.num_replicas_in_sync # num replcas is 8 on a single TPU or N when runing on N GPUs.\n",
    "learning_rate = LEARNING_RATE * strategy.num_replicas_in_sync"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "qhdz68Xm3Z4Z"
   },
   "outputs": [],
   "source": [
    "#@title visualization utilities [RUN ME]\n",
    "\"\"\"\n",
    "This cell contains helper functions used for visualization\n",
    "and downloads only. You can skip reading it. There is very\n",
    "little useful Keras/Tensorflow code here.\n",
    "\"\"\"\n",
    "\n",
    "# Matplotlib config\n",
    "plt.rc('image', cmap='gray_r')\n",
    "plt.rc('grid', linewidth=0)\n",
    "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
    "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
    "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
    "plt.rc('text', color='a8151a')\n",
    "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
    "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
    "\n",
    "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
    "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
    "  \n",
    "  # get one batch from each: 10000 validation digits, N training digits\n",
    "  unbatched_train_ds = training_dataset.unbatch()\n",
    "  \n",
    "  if tf.executing_eagerly():\n",
    "      # This is the TF 2.0 \"eager execution\" way of iterating through a tf.data.Dataset\n",
    "      for v_images, v_labels in validation_dataset:\n",
    "        break\n",
    "\n",
    "      for t_images, t_labels in unbatched_train_ds.batch(N):\n",
    "        break\n",
    "\n",
    "      validation_digits = v_images.numpy()\n",
    "      validation_labels = v_labels.numpy()\n",
    "      training_digits   = t_images.numpy()\n",
    "      training_labels   = t_labels.numpy()\n",
    "  else:\n",
    "    # This is the legacy TF 1.x way of iterating through a tf.data.Dataset\n",
    "    v_images, v_labels = validation_dataset.make_one_shot_iterator().get_next()\n",
    "    t_images, t_labels = unbatched_train_ds.batch(N).make_one_shot_iterator().get_next()\n",
    "    # Run once, get one batch. Session.run returns numpy results\n",
    "    with tf.Session() as ses:\n",
    "      (validation_digits, validation_labels,\n",
    "       training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
    "  \n",
    "  # these were one-hot encoded in the dataset\n",
    "  validation_labels = np.argmax(validation_labels, axis=1)\n",
    "  training_labels = np.argmax(training_labels, axis=1)\n",
    "  \n",
    "  return (training_digits, training_labels,\n",
    "          validation_digits, validation_labels)\n",
    "\n",
    "# create digits from local fonts for testing\n",
    "def create_digits_from_local_fonts(n):\n",
    "  font_labels = []\n",
    "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
    "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
    "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
    "  d = PIL.ImageDraw.Draw(img)\n",
    "  for i in range(n):\n",
    "    font_labels.append(i%10)\n",
    "    d.text((7+i*28,0 if i<10 else -4), str(i%10), fill=(255,255), font=font1 if i<10 else font2)\n",
    "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
    "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
    "  return font_digits, font_labels\n",
    "\n",
    "# utility to display a row of digits with their predictions\n",
    "def display_digits(digits, predictions, labels, title, n):\n",
    "  plt.figure(figsize=(13,3))\n",
    "  digits = np.reshape(digits, [n, 28, 28])\n",
    "  digits = np.swapaxes(digits, 0, 1)\n",
    "  digits = np.reshape(digits, [28, 28*n])\n",
    "  plt.yticks([])\n",
    "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
    "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
    "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
    "  plt.imshow(digits)\n",
    "  plt.grid(None)\n",
    "  plt.title(title)\n",
    "  \n",
    "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
    "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
    "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
    "  for i in range(lines):\n",
    "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
    "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lzd6Qi464PsA"
   },
   "source": [
    "### Colab-only auth for this notebook and the TPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "MPx0nvyUnvgT"
   },
   "outputs": [],
   "source": [
    "#IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ  # this is always set on Colab, the value is 0 or 1 depending on GPU presence\n",
    "#if IS_COLAB_BACKEND:\n",
    "#  from google.colab import auth\n",
    "#  auth.authenticate_user() # Authenticates the backend and also the TPU using your credentials so that they can access your private GCS buckets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lz1Zknfk4qCx"
   },
   "source": [
    "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
    "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZE8dgyPC1_6m"
   },
   "outputs": [],
   "source": [
    "def read_label(tf_bytestring):\n",
    "    label = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
    "    label = tf.reshape(label, [])\n",
    "    label = tf.one_hot(label, 10)\n",
    "    return label\n",
    "  \n",
    "def read_image(tf_bytestring):\n",
    "    image = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
    "    image = tf.cast(image, tf.float32)/256.0\n",
    "    image = tf.reshape(image, [28*28])\n",
    "    return image\n",
    "  \n",
    "def load_dataset(image_file, label_file):\n",
    "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
    "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
    "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
    "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
    "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
    "    return dataset \n",
    "  \n",
    "def get_training_dataset(image_file, label_file, batch_size):\n",
    "    dataset = load_dataset(image_file, label_file)\n",
    "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
    "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
    "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
    "    dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
    "    dataset = dataset.prefetch(-1)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
    "    return dataset\n",
    "  \n",
    "def get_validation_dataset(image_file, label_file):\n",
    "    dataset = load_dataset(image_file, label_file)\n",
    "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
    "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
    "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
    "    return dataset\n",
    "\n",
    "# instantiate the datasets\n",
    "training_dataset = get_training_dataset(training_images_file, training_labels_file, global_batch_size)\n",
    "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_fXo6GuvL3EB"
   },
   "source": [
    "### Let's have a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177
    },
    "colab_type": "code",
    "id": "yZ4tjPKvL2eh",
    "outputId": "11c2414a-ab78-4716-b3e1-5942a90239c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V4sqVK0hKSgIRgc/no7KyEvn5+aipqYGPjw+A2/ff19cX06ZNw4gRIwb8fFVVVeHUqVNISEiAu7s7IiMjMWvWLGN06b8CjUaDxsZGVFdXIzc3F3V1dbCysoK7uzvs7e0RFBQ0aEpVSUkJDhw4gOTkZIwfPx6PPPIIXF1djSqjsrISNTU1qK+vh1wuR3t7OzIzM5Gfn49bt26hvr4eQUFB8PX1ha+vLywsLMDn88Hlsl6jvUGj0SA5ORlxcXG4fv06dDodRo0ahYiICERGRsLb2xs83n+OapOQkIAhQ4bAx8cHlpaWD7o5RkEsFuPMmTMoKiqCm5sbxo4dC19f30HRPRQKBdLS0uDr64uRI0f+R13DiooKFBcXo6ioCG1tbSDqHCnc2dm515b8/5xfbC8hItTX1+OLL75AVlYWrK2tERISgmHDhiEoKAhqtRqxsbFISUlBRUUFbt26dd+V8cLCQvB4PIwcOXJQZBgMBjQ2NuLixYtIS0sDAMaiFxUVNSgPhEajQWpqKjZt2tSjMl5VVYXc3Fx4eXlhwoQJRm9HB3q9Hjk5Odi+fTsSExOxYsUKbNy40SiDsE6nw9mzZ7F9+3aIRCI89dRTcHJyuuc5Fy5cwM2bNxETEwN/f3/w+fx+t4GIoFAoUFpaCqVSibCwMKNNNHpDe3s7SktLUVVVhbS0NKSnpyMvLw9isRhtbW0AAIFAAJFIBJlMBq1WCwDYsmXLfW3nQLh16xa+//57JCYmorW1lbGOA4CJiQnS0tJAREhISIBAIEBVVRVee+01ODs7D0hxKi4uxqVLl9DY2IjFixdjzZo18Pb2Nla3/mMhIpSWliIvLw8FBQW4desWEhMTUVpaCgcHB4wePRpeXl5Yvnw5Jk2aNChtkEqlKC4uhlqtRmFhIdLS0oyujCckJOD8+fOoqqpCY2Mj+Hw+2tvbYW5uDm9vb0RGRmLGjBmYMGECgoKCBjROdAcRobGxERKJBBKJBCqVClZWVnBycoKnp+d/rbHAYDAgJycHu3btwsGDBxnlJzk5GcnJySgpKcHs2bMxatSo+7oi3hO7d+/GuHHjYG9vP+iKpEqlglgshkwmg6OjI1xcXCAQCIwqQ6vV4vDhw8jPz4elpSXmzZuHBQsWwMnJyeiyAKCgoAD19fVYunQpxo4dCysrK6PL6C16vR4SiQRSqRR1dXW4ceMGUlJSkJOTAx6PBxsbG1hZWUGhUEAsFmPo0KFwdXXF1KlT71l3vzQXtVqN5uZmKJVKmJiYwMTEBDY2NuDxeDA1Ne1Plb2mw/J89epVCAQCREVFYevWrXB2doaZmRnkcjlkMhlyc3MB4L4qBkSElpYW3Lx5E87OzggPDze6DJVKhfr6ely7dg3bt2/vdKy9vR2NjY2wtLSEubm5US0tKpUKSUlJjNLVHfX19Th//jyGDBmC8ePHD9r1b2lpwaVLlxAXF4e2tjb8+eefeP/99wesjOv1euTn5+Odd95BQ0MDoqOjYWNjc89BtKKiAuvXr0dbWxt8fX0xfPjwfr9kNRoN6uvrkZGRgT179qCxsRHx8fGDMtB1R2VlJTZv3oyjR492OePveO5VKpXRV0C0Wi30ej3a29vR1tYGjUYDHo8HkUgEMzMzo4wxarUae/bswYULFyCXy2FnZwcPDw+4u7tDJBLB0tISRAS9Xo+jR49CKpXiu+++Q0hICGbOnDmgSFNtbW1oa2uDnZ0dwsLCHpgirlKpIJVK0draytxfCwsL2NnZwdLSEgaDATqdDmZmZkZ5juvq6vDDDz/gwIEDkEgk0Gq1MDU1hVAoRGNjIxITE5GWlgadTjdoynhDQwOqqqoA3FbsGhoajC7jypUrOHLkCADAy8sLYWFhGD16NDw8PGBraztorosdqNVqVFRU4MqVK0hISGAmfl5eXoiIiMDq1asxZsyYfj9HWq0WTU1NqKur6zQucDgcWFtbg8PhgMfjwdbWFnq9HkTUaaI7EPR6PU6cOIEDBw4AAOzt7SEQCMDhcFBeXo5t27YhOzsbK1aswMSJE2Fra/tAjQPt7e3IysrCkCFDGPe+waBjtamwsBDHjx9HRkYGoqKisGjRIgwbNgwGgwGmpqZGGTulUim2bNmC2tpazJkzB0888QTGjRs3aNf5zJkzUCqVcHJyemBuXGq1GvX19ZBIJDh37hwuXbqEGzduQKvVws7ODqNGjcLEiRMRGBiI4cOHIz8/Hz/99BOqq6shFot7JaNfmsutW7dw+PBhZGRkwNbWFiKRCHPmzIGTkxOztNsdHQ+qQCDolwWXiNDa2gpbW1v4+flhx44dcHFxYX4IZ86cwYULF9DY2IhRo0YhODi4P13sF0qlEpmZmfjjjz+wfPnyQZFx69Yt7Nu3D4mJiSgqKup0LCUlBVqtFoGBgYiKioJIJDLKA0JEaG5uxqVLl+75MA8ZMgRWVlaoqKiASqUa1OXmnJwcNDc3w8nJCTNnzjSKstrQ0IB169ahrKwMjz/+OP72t78hMDCwx3P0ej2+++47VFRUYN26dQgPD++35YmIUFBQgD179uDXX39FXV0drK2tIZVK4e7uPmgDnl6vZ5RePp+Puro6/Prrr9Dr9QAAU1NT8Hi8TpPvYcOGwdPTs1M9A2mfXq+HWq1GaWkpMxm5cuUKysvL4ezsjAULFmDs2LHMRGcg1r3S0lKcP38ecrkcDg4OWLlyJRYtWgQnJydYWVkxLkk6nQ5eXl745JNP0Nraih9++AE+Pj4YPXp0v15sRIS2tjaoVCq4uLjc1/HpTjQaDa5du4ZPP/0U58+fZybZ4eHhWLFiBaKjo9Ha2gqpVIqJEycO2Bql1+uxb98+bNu2jVFKBAIB/P39GR96jUYDhUKBuLi4AfevN1hZWQ3KOD1+/HikpaWhrq4OMTEx93W1SKVSISMjAy+//DJycnJgYWEBDw8PuLm5oampCYcOHYJIJIKNjQ18fHxgMBhgYmLSJ8ONRCLBnj178Mknn0Cn0zHfC4VCzJo1CxwOB87OznjiiSfQ2toKjUYDHx8fDBs2DHw+nxlD+mMs4nA4zL4ogUCAZ555BgEBAeBwODh8+DBSUlIQGxuL4uJirFixAs8888ygbfYjImg0Guj1+m4nrIWFhZDJZEzbBwO9Xo+srCz8+OOPOHbsGOrq6pgN4lKpFPPmzYNSqcSwYcMwZsyYActLS0tDc3MzeDweFixYgOHDhw9a3wwGA86ePQuVSgU/Pz/Y2toOipx7UVhYiE2bNuGPP/4AcPu37ufnh8DAQKxYseIu1zoOh4OIiAio1WosXbq0d0Lkcjl19+mOL774ghwdHYnL5RKPxyMul0tcLpesra0pPDy8x8/UqVPpjTfeoKysrG7rvxcGg4EqKipIJpPd9f3atWtpyJAhxOFwaOrUqZSent5vOX2loaGBvvnmG7K2tqZLly4ZvX6DwUBff/01cTgc5pp3fDgcDvF4POLxeOTp6UkHDhygpqYmo8iVy+W0d+9eMjMzow0bNvRY9tq1a7R06VKaNGnSXffHWGg0Gvr000/Jzc2NAFBAQIDRrnd8fDxZWVkRj8ejuLg4UqvV9zwnMzOThEIhzZ49m3Jzc0mn0/VbflNTE3322WdkZWVFAIjD4ZBAIKAlS5aQVColvV7f77q7Q6VSUVZWFn344Yd09uxZIiJKSUkhJycn8vT0JEdHR5ozZw6tX7+e9u/fT1lZWdTU1ERKpdJobdDr9ZSZmUlvvPEG+fr6kkAgIA6Hw3zuvBaBgYG0atWqAclPTU0lV1dX4nK5tHLlSsrMzOyx/IYNG8jMzIwA0LZt26iurq5fchsbG2nNmjXk4uJC77777j3LGwwG5mMs2tvbKTY2llxcXIjL5ZKpqSnx+Xzm34iICJo/fz75+/uTv78/nThxYkDyDAYDZWVlkbOzM/O+MDU1peXLl9ONGzfo4sWLtGDBAuLz+cTlcmnIkCEDeoZ64quvviIABIC8vLwG5XlqaWmhDRs2kJubGy1atIgkEonRZXSFWq2my5cvU0BAAPF4PBoxYgSdOXOGeZfX1NTQ4sWLKSYmhmJjYyk7O5tiY2OpvLy8T3Jyc3Pp1Vdfvesd1PEe6vjw+Xzmb1dXV1q0aBF99913FB8fT3l5ef269iqVimbNmkUcDoeefPJJamtrY47l5eXRihUryNramjgcDjk4ONBrr71GKpWqz3J6Q11dHe3evZveffddqqmp6bJMeno6OTk50ZYtW6i2ttbobdBqtXTz5k1ydXVlxsk7PwKBgIRCIdnY2NDixYuNMo6sW7eOhEIhxcTEUFFRkRF60T0VFRVkbW1NLi4ulJaW1m25O8dJvV5v1PGSiCg2NpZsbW3JwsKCnn32Wbpx4wZpNJpuyzc1NZFEIumyjFar7VLf7pcyXlxcTAcOHKBdu3bRsWPH6NNPP6Unn3ySXF1dCQC5u7t3+mFwuVwSiURkbW3NHP/ss8+McpHuZN++fTRmzBji8Xjk6upK69evH7QHsSuysrJo7NixFBgY2GmQMBa7d+8mf39/RjG58+Pg4EBeXl6dBsRTp04NWGZLSwsdPnyYHBwcaMyYMaRQKHosv3//fho5cuSgKuNFRUW0ePFi5jpMmTLFKPVKpVJas2YNCYVC+vzzz3ulcGVnZ5OzszMBoG+++WbAE6Djx49TdHQ08+zceY9FIhF98cUX97wHfUGr1VJ8fDz5+fmRo6Njp99MeXk5GQwGqq6uHtTnKDMzk1avXs1MogGQnZ0dTZkyhVavXk3z588nGxsbCg4OJg6HQy4uLuTh4UHbt2/vt9J2+fJlcnFxodmzZ1NqamqvzulQch577DHKzs7ul9x//etfNGbMmF4p42lpafTHH3/QwYMHKTk5mRoaGvol807UajWdP3+eUZ5cXV3p999/p/j4eDpz5gzt27ePfv75Z1q2bBnZ2NhQaGgopaSk9FueXq+n7Oxsmjt3LjM2eXh40NGjR6m5uZkpU1dXRz/99BMNGTKETE1NaezYsQPua1ekpKTQokWLCACJRCL69NNPB0XOuXPnaO7cuTR16lTKz88fFBl/JTc3l5599lni8/nk5+fXpSGhqqqKKioqKCUlhZYtW0YcDoeioqL69Hzr9XoqKyuj7777jv7+97/T9u3bafv27bRlyxaKjo6mKVOm0JQpU8jDw4MEAsFdCmLHWPaPf/yjz31sa2tj6rh58+ZdCn1ZWRl9/vnn5O/vTxwOh6ysrGjlypWDMrm7ceMGPfPMMzRhwgQ6f/58l2UOHz5MdnZ2tG/fvh51qr6i1+upurqa/v73v3e6ttbW1sTn8++65nw+nyZMmDDg6yCVSikoKIj4fD7t27ePeYYHi3Xr1pFAIKDly5d3O2lUqVR0+fJlOnDgAB04cIDeeOMNunz5co/Kcl+5du0aRUdHk4ODA/32228DqsuoynjH7KPjo9PpSKPRUG1tLcXFxVF9fT2dOXOGTp8+TadPn6aEhAS6desWffTRR2RpaUm+vr60f//+AXXor1y4cIGCg4OJz+eTtbU1LV++vN8vzP7Q1tZGx44dI4FAQOPHjzd6/Xl5efTKK6+Qg4NDJ8u4UCikNWvW0NGjRykhIYE++OADEolExOVy6dVXX6Xc3NwByU1LS6OYmBjy8PCga9eu9Vi2ra2NNm7cSAAoMjJy0JTx/fv30/jx4wkAOTs705tvvjngOg0GAy1btox4PB4FBQVRfn7+PQcuvV5PO3fuJA6HQ0uWLKHGxsYBtaGhoYFef/11MjMzIxsbGxo7dixFRkZ2UshdXV2ptLTUKBY9nU5HJ0+epMjISHJxcaFNmzYZVdG/FyqVirES83g8AkARERG0YsUKunr1KimVStJqtZSQkEDPP/88nTt3jkJDQ8nMzIy8vLzI1NSUKisr+2UFmTt3LgmFQlq3bh2VlJT06pxXX32VLCwsaNiwYXT58uU+y+yow8bGhqZPn86sQvyVtWvXkp+fH9nZ2ZFQKCSBQEAikYjmzJkzICu1UqmkixcvMhbx6dOnU3JyMmk0GmYsr6+vpw8++IA8PT0pICCAvv/++wFZmbRaLe3du5e5v3w+n9avX0/V1dV3lS0tLaUVK1YwY9uaNWv6Lbc7OlYWB1sZT0hIoDlz5lBoaInfV7EAACAASURBVCgdPXqU0tPTmU97e7vR5RUXF9P69etJKBRSYGBgj2NvZWUlrVy5kiwtLcnd3Z3efffdft1jnU5310ej0TCf3Nxc+vHHH+mzzz6j559/nry8vDop5E5OTn1W5pRKJU2dOpU4HA4VFRV1OQ5WV1fT559/zljIbWxs6MiRI33u3704cOAAhYeH05QpU7rVNR599FFydHSktLQ0o1prZTIZLV++vNNz9cUXX9CxY8foscceI5FI1Mk6PmXKlF4bHbrDYDDQl19+yehYp06dGhSj450sWbKE+Hw+ff31110aI1pbW+nEiRPk5eXFeAZwuVzi8/l08uRJo7RPLpfTzp07SSQSUWRk5IAnIN0p4/3yGedwOHf5CJmYmMDR0RFTp04Fn8/HlClTOh3Py8tDaWkpeDweQkJCMH369P6I7pacnBw0NTVBp9MhMDAQM2fOHLRoJl0hk8mQnZ0NDodjdL+m+vp6bNu2DUeOHEFTUxOA2z68Xl5eiImJwTvvvMP4h7u5ueHIkSNQKpXYv38/1Go1XnjhBYSGhvbZR6+iogKxsbHIyMiAv78/xo4d22P58+fPIykpCY6OjggKChq0neP5+fmQSCTgcrkIDAzEs88+a5R69Xo9DAYDs1Gku+tF/96o+/3332PHjh0AgM2bNw/4vldVVUEsFsPGxgZPPvkk3n33XTQ3N+PIkSPYuXMnysvLUVNTg1WrVmHHjh3w9PTsd+QcjUaDxMRE7NmzB5mZmRg3bhyWL19+X6IsEBGamprwww8/YN++fWhvb2ei1qxatQqurq6wtLRkNnzp9XooFArY2dnhlVdewbJlyyAWizv5q/aFyspKVFVVQSgUwsfHp9dRFyIjI7Fv3767NrP2Fo1GA7VaDXNzc4SGhiIqKqrTsY6NZ8XFxUx40AkTJqCurg5isRjJycnw9PREREREv/IJSKVS7NixAzKZDDNmzMAnn3yC4ODgThvrJBIJcnJyUFNTg8mTJyMkJGTA+wDOnTvH7D0ICwvDypUru0wM4uHhgTfeeAOXL19GUVERE83GmP6oBoOh3/evtygUCqSkpODGjRtoamrCiy++CC6Xy/Rj9uzZWLhwIWbOnGm0yFcymQzl5eXg8/nw8fGBSCTqtqydnR2EQiF0Oh14PB4sLS37dY27avud340YMQJeXl4wGAxQqVTMO/Knn37CyZMnIZPJsGPHDvztb3/rtUwulws/Pz9cvHiR+e7EiRM4deoURo8eDU9PTxgMBpiZmWHKlCn4888/odFoUFJS0uf+9YRarUZBQQH0ej2mT58OPz+/bstxOBzw+Xyj/Y7Ly8vx3nvv4eTJkzAYDBg9ejQ+//xz3Lp1C19//TVu3ryJ5uZmprytrS3GjBljFH/x/Px86PV6uLu7Y/To0YMe173jWe3u2kmlUuzduxcVFRUAgA8++AAhISFYu3Yttm7dioCAgAFvkM/Ly0NCQgJMTU0RGRk5aNFcjBrakMPhMJua7tzcpFAocOLECSQkJMDHxwePP/64UTdVbNiwgdm4EBoaiueffx5Tp069r7FGW1paUFZWBisrKzz11FNGrVuj0UAikaCpqQkGgwEikQhTp07F22+/DU9PTzg4ODCKo7e3N55++mls27YN9fX1OHbsGJydneHj4wM7O7s+yS0vL0dycjL4fD7mzp3b4274pqYmJCUlIScnB35+fpg/f/6ghOwqLi7GjRs3UFdXBz6fD2dnZ6NPulJTU/Huu+/C0dERkZGRnRLZtLW1ISsrC2lpaTh37hwaGhowbdo02NnZDXiwVavV0Gq18Pf3x6xZs+Do6Ag7Ozs89dRTiI+PR3l5OYgI7e3tXU6I+0Jubi5+++03pKSkYPLkydiwYQMcHBwG1P7e0t7ejuTkZOzcuRMKhQIODg7YsmULpkyZAhcXF+Z3Q//e7CgUCuHo6Ig333yTiX6h0+kQExMDGxubPl+HU6dOoby8HIGBgQgNDe31JKojakN/uXTpEvLz8+Hq6oqRI0fC1NQUOp0ONTU1OH78OHbu3Ini4mLY2Njgsccew8KFC5kN8fv378fBgwdRXV2NpqamPivjRUVF+OabbxAfHw8rKyu89dZbCAwM7PSM6vV6xMXFIScnB/b29hg/fjxGjx7d7/52TLrS09M7KaJDhgzpcqJrYmICJycnzJo1C0VFRf+VyT0UCgW+/fZb7N+/HwaDAYGBgfDy8oKvry90Oh1OnjyJlJQUXLp0Cba2tvjqq68wevRoWFpaDij6VUtLC6RSKezt7bF48eIef6eFhYUQi8UwGAwYOnQoQkND+y23J0xMTBhlzcLCAiKRCHZ2dtBoNDh58iTMzc0RHR3dpzo7DDDA7XcBl8vFli1bkJubi9OnTzOb+HU6HZMPQafT4ciRIwgJCcGECROMYiSSSqUoLy+HqakpRowY0eW7TqFQoKysDEFBQUaLAlJdXY0DBw4gNjYW7e3t8PDwwAsvvIC///3vaGhogEwmQ3t7O6PEWltbIzo6Gs8///yA9SEiwqVLl0BEmD9/fr/GXmPS0tKC1NRUnD17FgKBAB9++CGWLVsGS0tLjB07FhcvXmTC8A4EtVoNhUIBgUAALy+vQevzfdFWb9y4geTkZDQ0NGDs2LHw8/MzmkVALpcjLi4OJSUl4PF4iImJQXR09H1NlyqXy5Geno709HT4+PgYPXQV/TvEGhHB2toakydPxpo1axASEnJXRAdTU1PMmDED586dw9WrV9HY2AiZTNZnK6JWq0VVVRUKCgpga2uL+fPn91j+xo0bSE9Ph0ajgb+//6AN8EVFRZBKpdBoNBg5ciRCQkKMEkWFw+EgJiYGcXFxnUIJXr58udNMWKvVor6+Ho2NjWhsbISTkxPWrFljlIlHfn4+qqqqwOfzmRUQHo8HOzs71NTUgMPhgIjg6+sLKyurAce6LiwsBJfLRVBQEMaNG3ffko7o9XrI5XJGse6IRHDlyhXU1tZCr9eDx+OhpaUFlZWVTFzXvLw8JuqHjY0NVq5c2a+X3KlTp2AwGDBnzhy4u7sPSlz+rsjPz0ddXR08PT2ZuPUymQy//vor9u7di4KCAowePRorV65EZGQkkySEw+EgICAAQ4YMgVar7deKQENDA65evYrW1la4ubkhLCys03NjMBhw5swZnD59GtXV1Zg0aRKmTZs2IMuXTqdDUVERysvLAQB+fn6YPn16j3WamJjAzs6OmXTK5fIHFkGhP9TW1uL69evQ6/VYsmQJ5s2bB2tra4hEIhgMBkyfPh0NDQ04c+YM0tLSsHHjRvj4+GDp0qUYN25cv37Per2eye9ga2uLESNG9Fj29OnTyM7OBgA4ODjcMwKasTAxMQERQSqVArit5JSUlPQpH4WJiQmio6Px8ssvY/jw4TAYDJBKpZDL5WhsbOzyHJ1Oh9zcXGzcuBHu7u5YtWoVQkNDYW5u3m/Fqry8HBKJBPb29t1m571y5QqkUinmzJljlBVHIkJZWRkOHz7MWL6bmppw8OBBZGdnQygUIjo6GtXV1SguLoZCocCwYcMwderUbi33fUUul4OIIBaLceTIEbS0tKCgoAByuRzA7SgjXl5emDVrFiZMmDCoyrpYLEZ8fDyICGvXru2UD8TCwgJKpRIZGRlwd3cfUCjauro6lJWVMQaKwWLQlXGpVIoTJ04gOzsb/v7+eOSRR4yaMvvnn39GaWkpNBoNgoKCGEvm/cxmVltbi/T0dEgkEsyaNQtDhw4dFDl6vR6hoaFYvXo1IiIiug2t5uTkBDs7O/B4PBARysvLkZeXB0dHx17L0ul0zCw7LCwMXl5ePZYvKipCTU0NrK2t4enpOWgv0JKSErS2toLP5yMwMBCRkZFGqzs6OhpvvfUWCgsLUVZWhrS0NDQ2NjKrD8Dth9zb2xu//PILOBwO/P39jZb11NzcHAKBABKJBLdu3WKU0OzsbNTU1EAoFEKpVKKiogKNjY0QiUT9tnZ0KP5KpRKVlZXIyMjA8OHD74t1vMPFytvbG/n5+WhpacEvv/wChUKBxsZGEBG4XC7a29shk8mgUqmYmOY8Hg+BgYFYuHAhoqKi+tX/yspKODo6YsqUKQMapPuKVCpFe3s7BAIBBAIBNBoNSktLcfToURQXF2PChAlYvXo1YmJi4ODg0OlFJhQKYW9vDxcXl34ZGrRaLVpaWpgl8ztdc5qbm5Geno49e/YgOzsbBoMBI0aMGJBVvEPmrVu3GOvUE088AV9f317fs7q6OlRUVBgtROtfEQqFRo9nbmVlhQULFmDmzJkICwu7yzXA19cXSqUSw4cPR2FhIU6dOoW0tDSoVCq0tbX1K4ykwWBAS0sL6uvrMWTIkG4zBRsMBlRVVeHatWsQi8VwcXFBUFDQPROaGQutVouSkhImPByPx+vTOwm4bTQZPnw4XnzxRXh6eqK9vR2PPvookpOTIZPJUFtbyyiGd6JQKJCUlAQ+nw+lUomJEydi3rx58PLy6nOIUoPBgPT0dIjFYgQEBKCxsRHHjh1jciJUVlYCuB0CsK2tDSKRyGgTfrVa3Sk2fnt7OzIyMjBx4kRER0fD19cXO3fuhF6vh0gkQnh4OCZNmnTP7NF95cqVK8jPz0drayvTX+D2Kr6LiwuzijvQXCsd1neFQsG4unXIKSoqQkpKCvz8/LB8+XK4u7vfdX5tbS2USmW/x3m9Xg+ZTAa5XI4xY8ZAJBKhrq6OOd6hZxmDQVfGs7OzkZSUhLa2NkRHR2Pu3Lk9+rP1herqauzcuRMtLS2wt7fH3LlzMXLkyPuaHAW47dNdXFwMPp+P0aNHG93SJhaL0draCgAYNWoUYmJieiwvkUhQX18PtVrNWJg6zu8LXC4XlpaWcHBwgFar7db6q9FoUFxcjIaGBri5uRl1snUnMpkM6enpaGxshFAoxPDhw43qouLi4oJXX30VlZWVyM3NxYULF6BWqzFixAimT+bm5jAzM8Mvv/wCe3t7zJs3r8/uP93RkTAgLy8PSUlJMDMzQ1VVFW7evAmRSITJkycjNjYWN27cwJUrV+Ds7NzvQaasrAxSqZRJCc/j8TB27FhERUXB3t4eCoUC1tbWzGDY2toKCwsLWFpaDnjwEQgECAgIwLPPPouMjAzcvHkT6enpEAgEjLVKLpejqakJKpUKAJgEMSNHjsRzzz2H5cuX92sc6dgXYG9vD3d390FPUnYnLS0tnVwvZDIZrl69isLCQgwfPhyrVq3C008/3eX40RHL2NXVtV/+4jweD+bm5sx4cPr0aeZYdXU1zpw5g7Nnz6KlpQXu7u7w9vYe8OqiXq+HWCxmlswXLVoEKyurXinW9O/cBh2JZQZDGTczMzO6Mu7k5ISnn376nnLDwsIQFhaG4cOH4/z58zh58iR+++036PV6hIeH92lM6ZhgmZqaQqvVoq6uDv7+/p3KqFQqlJWV4eTJk8jLy2PGtcjIyPuWjbO5uRkZGRlISUkBj8dDQEAAIiIi+lyPQCBgXFVMTU2xbNkyBAUFoaGhAaWlpZBKpdDpdNDpdOBwOBAIBCAiZGdno6SkBMePH0dqaioA9Esh12q1yM7ORlVVFSwsLHDgwAEUFxcDuH0vOv6WSCTQ6XS4efMmioqKYGlpOaCVJg6Hg6FDh2Lu3Lm4ceMGeDweHBwcYG9vjxkzZmDatGm4ePEi0//IyEjMmjWrW8v9QNpRWVkJhUIBd3d3hIaGMr76BQUFqKiowLFjx8Dj8eDq6tqlktxbxo8fj7179+LatWtYtGgRM3kTi8VISUlBfX09Hn744bss/+3t7RAKhXB1dR3QNe+Y5KpUKjQ1NeHo0aOdjo8cORJBQUFwdXUd8Or4oCrjRMQkPxg5ciTCw8Ph4uJilLq1Wi1OnjyJsrIy6PV6BAYGYt68eYOmCHZHxzKZWCyGo6Njp01ZxiIxMRFisRgmJiY9vpR0Oh2USiXOnj2L/Px8qFQqxoIcFhbWJ5mmpqZwcnKCSCRCXl4eMjMzMWzYsE5llEolM1NPTU2FVCpFcHCw0e7xXyksLMTVq1chk8ng6uoKR0dHo28SNTc3x8iRIzFy5EgsWLDgruOtra2IjY0Fn8/HpEmT8NxzzxlNtqenJ4KDg3HlyhUkJibiwoUL4PF4GDp0KBYsWIB169ahvLwcqampOHHiBCZPntxvdxUzMzPw+XxoNBqIxWL88ssv+OOPP/DII49g1KhRqK2thZubG7y9vcHhcFBdXQ03Nzf4+PjAy8trQIpaxybnt956C62trdi3bx+ysrJgZWUFR0dHxvKUlJSE6upqALdXJHx9fbFs2TK88MIL/b7vNTU1aGtr65cC0rFE21/FsCNBSIdyWltbi0uXLjEK2LJly7o8T6fTob6+nrGq9+d+29vbY8KECSgrK0N9fT1ef/31Tv0SiUQQCoVMco0On/aBYDAYmIQnwO0X1/1csfxvYOLEiQgKCoJKpcKff/6JlpYWqFQqREdH91oh53K5cHNzQ1BQECorKxEfHw9nZ2fY2tpCqVRCr9ejoqIC8fHx2Lt3L2pra8Hj8ZjkVfcDvV6P8vJyXLp0CWq1GpaWlli0aJFRfKn9/Pzg5eUFPp8PvV4PnU7HJJDqSFBmMBiwe/du/P7778jPz0dNTQ327dsHPp+PBQsWwMPDo099USqVaG9vR15eHhobG+Hs7AwvLy84ODgwgQ52796NlpYWxMbGwtPTE87Ozne9Q/vKiBEj8N577yEzM5O5hx2J18RiMfbv34/a2lrY2tpi7ty5iI6ONureLQcHB0ilUgwdOhQPP/wwpk+fDhcXFwwbNgxEhGvXruGPP/7AoUOHcPDgQTz00ENYsmRJv+XNnTsXGzZswPHjx7FixQq4ublBKBQiPz8fiYmJsLa2vst1RCaToaqqCj4+Phg/fvyAVj+bmpqYvQelpaXYtm0bgNsrFG1tbfDw8MBTTz2F2bNnY9SoUQNS/AdVGZfJZLh+/TqUSiVmzJiByMhIowzGHT5LO3fuhFqthlAoRHBwMOzs7O77YN/U1ITCwkI0NDRgypQpfVZ6e0NmZibq6+vvWa6yshKZmZn4+eefIZPJwOPx4OTkBEdHxz4/kCYmJnBwcICDgwOuXbuGt99++66HqqSkBJWVlaiursatW7cYC42xl8Q6qK+vh1arBYfDwYgRI+6br2MHHS+Un3/+GWZmZnj11VeNtsoD3FaQFy5cyGR6bGlpgUgkwsqVK7Fu3ToIBALMmzcPubm5SE1NRXZ2NpydnXsdDeROpk+fjtLSUqSkpDDp7hUKBQ4ePNjtOdbW1vDz88PKlSvx9NNPG+U+W1lZ4aWXXur0XXt7O7766iukp6cDuK2Ih4SEYN26dXjkkUcG9IyfPXsWYrG4z9ZlrVaLc+fOQaVSwdraul+KaocSf6cy36GYSyQSZvXhr/0Ti8W4fPkyamtr++1G5OvrizfffBNyuRwXLlxgllo70tGvW7cOSUlJSEhIQEhICIKCgvol5060Wi0KCgr6NXnhcDj9ztL434alpSWeeeYZALf3M3z11VdQKBS9jhLF5XIxbdo0qFQqvPbaa/j222+RkpKChx9+GEVFRVCpVMjOzkZrayvj6mVnZwc3Nzejjl/dQUSoq6vDhQsXOllM77UPqTeo1WpUVlairKwMI0aMgLu7OzMu/XUy8+abb2LKlCnYsGEDLl++jJycHPz888/w9vbG0KFDe73i1xGxZtSoUfDz88PSpUsRFRXVSekzGAw4dOgQ1Go1nJyckJmZCZlMNmBl3MTEBM7OzndFIyIixMbG4sKFC2hra8P06dMHrIj+FQ6Hgzlz5iAvLw8RERHYtGnTXUaZefPmwcXFBampqRCLxaitrR2QTEdHR4SHh+P06dM4evQoHBwcMHr0aKhUKmg0GggEgk7XQqlU4ujRo8jOzsbGjRsHvGpta2uLsWPHYt68eRCJRBg6dCgMBgMkEgmys7ORm5uLf/7zn0hOTsamTZv6nZkZQP8ycPYGvV5PmzdvJhcXF5owYQLFxsYOqL47UavV9PbbbzOxl+fPn0/l5eWDkk3tXiQkJNDs2bPJy8uLPv7440GRsWTJErKxsSEej0evvfZal2XUajWtXbuWhEIhE2/TxcWFPv74Y6qoqOiX3KamJjpx4gTNmTOHbGxsSCgUdvq4u7uTp6cnWVpaMvE9161bZ9Rg+3eydu1asrOzI0tLS3r//fdJLBYPipzuqKqqoo8//pi4XC65u7vTzZs3B03O3r17ac2aNbRp06ZOsVKVSiUtXbqUzMzMKCYmhjIyMvolw2AwUFVVFX3++ecUEBBA3t7eTEzrv2bS++snKiqKjh49aqzudkKv19OPP/5IgYGBTDzoiIgISkhIMEr9//rXv4jH41FoaGiXsa67wmAw0LVr15gMktu3b6f6+vo+y169ejU5ODjQo48+SufPnyeNRkOJiYnk5uZGIpGIPvroo07ZGvV6PWk0Glq7di15eHjQ6tWrex0TvTtUKhVduHCBtm7dSlu3bqXTp0+TTqej9evXk6OjI7m7u9MPP/xglPi89fX19NBDDzFxw7Va7T3Pkclk9MEHHxCHwyE7OztKT083anzmv2bg7E2b7hcKhYK2bt1K7u7uNH369D6fL5VKaffu3TRhwgRmjLaysqKgoCBauXIlnTx5kkaPHk1cLpcmTZo0KLG3u6KlpYV++uknGjduHAEgR0dH2rVrl1HqPn36NIWGhpKVlRW9/fbbVFBQcM/fS2trK0VFRZFQKCQLCwt68803qaCgoE9ylUolyeXybp+TyspKsra2pjFjxlBiYiIVFhYOakzu2tpaGj9+PJmampKrqyvFxcUZXR8yGAz0xRdfMLk4uhs/CwoK6PHHHydLS0v68ssvByz3+PHjTELJsLAw2rp1K7388ssEgMaMGUNisZh0Oh2pVCrat28fubu7k7e3N928eXPQdBGi27+B9PR0WrBgAZPvpTcZSY0aZ7w3nD17Fps3b4ZCocCSJUswbtw4o9Wt1WqZ5QIA+Oc//3nfN2120NTUBJlMdtemqPuJXq/HY489xsQA7aDDn6svS3B3IhKJMHfuXEyfPh05OTlMLM8OHnvsMfB4PHz44YfYs2cPxGIxeDzeoIQ07Aj/JpPJ4OnpiVGjRsHV1dXocnpCKpXi6tWrMDc3x6xZsxAcHDwoctzc3PDMM88w1rI7EQqFeOyxxxAfH4/09HRUVFTA39+/z/skOmLSr1u3DuvWrUNrayuuX78OhUKBTZs2MT6VXVFbW8tEyDA2hw4dwpYtW1BQUAAiwpAhQxAeHj4o7l+9gYiQnp6Obdu2oaGhAePGjcNDDz3UZzeduro6SCQSxgceuG1hi4iIwJ9//olly5bho48+QkJCAk6cOAEbGxucP38e3377LZKSkjBp0iQsXrx4wJY1gUCA6Ojou0LK/fbbb6ivr0d0dDS8vb2NFoqtr2OyQqHAoUOHwOPxsGHDhnvmNugrrq6uCAgIwK1btyCXy7F582asX7/eqDL6i4WFBbPiZzAY+ny+o6Mjli9fjieffJKJlmJqasq8e8vKypiITMHBwQgICDBq+7vj3Llz2LNnD9LT0yEUChEQEIAVK1YYpW6FQgGpVIq2tjZs2bIFWq0Wr7/+eo8BBywtLbFt2zY8+eSTKCgoYGKU9xSF5q8IhcJuVwaJCGfOnEFraytmzJiBMWPGDLpu8PjjjyMjIwM6nQ7Lly+Hv7//oOhD5ubm0Ov10Gg00Gg0d7ntyeVyXL9+HefPnzdaPP9HH30U9fX12LVrFzIzM3Ht2jXmWFtbG5KSkqDRaHDs2DHGp/v8+fMYNWrUoOgiHQiFQjg5OUEsFjMbZgfi2jcoyrhMJsM777yDxsZGTJ06FZMnTx7UHdtNTU1obGzstPFJIBDAzMwMXC6X8R8jIsjlcnz99dfMD8XExAQvvPACAgIC+rXxMjc3F3l5eQgKCsLEiRON1qe/Qv8Ob1heXo64uDiUl5fjk08+YZabO9w37mTDhg2YOXPmgGWbmZlh/Pjxd4Ur7JA3dOhQ5uVdX18PsVhsdN/9Dncn4PamjoEG8u8PEokEqampcHZ2vsu14n4yf/58nD9/Hr///juOHDkCd3f3AU92rays8NBDDzHJZ1JTU8Hj8bB06VI8++yzOHHiBA4fPsz4cA8G6enp2LhxI0pKSqDT6SAQCDB79my8+OKLRhtUvby8+pTkRKvVYsuWLThw4ACcnJyY8Gh9db1wdHTEsGHDcO3aNRQVFSEvLw+RkZEwNTVFcHAwrl69ivj4ePj7+6O1tRUbNmzA3r17odFosGjRIrz66quDFi60qqoKer0eFhYWmD9/vlFcVIDbkRA2btyIrKwsNDQ04MUXX8R3333Xo1+lXq9HU1MTHB0dsWrVKqO047+F2tpaXLlyBSUlJZg2bVq/6uBwOLCwsOgyioVEImFCg3p5eQ1oY11vuXLlCnbt2oVLly6Bx+NhzJgx+O6774y2IXfhwoWQyWT49NNPUVFRASLqlRKYl5fHvE9cXV2Nrp90hKadPHnyoAeUKCsrQ05ODnQ6HaZOnYqnnnpqUO4th8PBmjVrcOPGDfz22294//33sWHDBvj6+oLL5aK1tZUxwsrlclhaWvZrs3lXLF++HI888giOHTuG3bt3IyMjA8DtKG5PPvkkUy48PBy//vorPD09B9U4S0S4fPkyXn/9dWRmZiIkJASLFi0aUCQ9oyvjBoMBixYtQl5eHlxcXLBmzZo+zTj7w6xZs+56uEeMGIFx48bB0tISYrEYcXFxAG5fxDutU8DtF+5LL73U580shYWFKCwsZBz8Byum5iOPPIK8vDxkZWUhPj4eFy9eBBExm3M6uFP+vHnz4OnpadQ23asuIoK9vf2ghHbsyK5qY2ODRx99lNlNf7/Izc1FfHw85HI5PD0977v8OzExMcGKFSuQlJSEw4cPIyAgAF5eXgP2j+vwUivFnAAAFuZJREFU+Z82bRo++OAD6HQ6/P7776iurmZ8HoH/TeBhbBISEphIAAKBACtWrMDq1auNOn5ERUXB1dUVCoUCMpkMjo6Od/mKKpVKZGZm4vfff0dWVhaSk5NhMBjw7bffYsqUKf22Gr/xxhtoa2vD8ePHsWXLFuTk5DDReHQ6HdLT0/Htt98iLS0N7e3tGDp0KNasWYOFCxf2e3WrN3z11VdoaWlBeHg4wsLCjPYC5fP5CA0NxT/+8Q+89NJLOHbsGFauXInQ0NAuJ1dlZWX46KOPIJfL8eijjxo9u19ZWRmSkpKQl5dn1Hr/ytmzZyGRSBAYGNhry35DQwMOHTrE+OwPhrU+ISGBCfs30KRhvSErKwubN29mEsWEhITggw8+MFrMa+B2Px5//HFmU/uhQ4dQVFSEWbNmYeHChXetnu7atQu//vor8vPzmfHszuyoxmagicJ6gohQU1ODefPmobm5GS4uLvjwww8HNTENh8PBJ598gqSkJBw/fpzJ12Bvb4+EhAQcOHAAMpkMvr6+WLx4MRYvXmw0uc7OznjhhRfw9NNP46effsKHH36IpqYmWFlZ4bnnnsO4ceOwYMGCASfQ6ooOY6iJiQmam5uxdetW7Ny5E42NjQgODmYmJQOJNGZ0ZbyyshLXr1+HRqPBSy+9hLCwMKNHvDAxMUFUVBTOnTsHAJ1Sv3aQmZmJgoICcLlc6HQ6tLa2gsfjMTcpKioKo0aNgomJCSZMmNCv6BCZmZkoKSmBSCRCYOD/a+/eg6I6zweOf3cXWOWywoLclovcFjELw0UEJIiXBFEgxRqjmNiGdDRppBPToA5pZpxOU9OOdTJT27GTGSdTO6mooWNME0iISsNNaQwE7xqMghIkVhB3ubjL7u8PfruDEW2Ne/ak5f3MOArj8Jz3cN73PPteE+/aTspZ5s6dy/vvv+84JntkZMTRi69UKrFarSgUCvz9/YmKiqK8vJzExETHKmupja/4SqVSkk+kzc3N3L59m+DgYAIDAyVbJHovZ86coa6uDo1GQ0ZGBiqVCpPJxMDAgGS7x9zPI488QmlpKTt27KC6uhq9Xk9RUdFDbztoP246Pz+fmpoaRkZGaGpqYmRkBKvVSnx8vGP1uDMNDg6ya9cujEYjMDb6sXjxYuLj4536PNnbgIsXL7Jx40bCwsLuSgyNRiPt7e1cvnyZ4eFhpk6dytKlS8nKynqo6RuhoaG88sorqNVq9u/fT2VlJdXV1SiVSseWgyaTicHBQYqKinjppZcwGAz4+flJdjBRR0cHH3zwAUNDQ6SnpxMUFOTUF7n9COmCggJqampoaWm56+RPGBtNra+v58iRIwQHB7N+/XqnXYNdfX09dXV132kKyH/qs88+Y+fOnQwPD+Pj43PPZLyvr4+6ujosFgstLS20t7dz48YNDAYDq1atcvrI37Vr1/jwww/p7+8nLi6OsLAwSbf1NJlM/OpXv3Kcgjhz5kxWrFjBo48+6vT3g6+vL6+++iomk4na2lqamppobW1l586dd70nrl27Rl9fn2OKRWRkJMuWLZP0MBepWK1WTp486ZjSV1xc/J2mLD6o4OBgtm/fzqZNm6itraWhoQGVSoXRaMTNzY2CggKee+65CQ8lfBj2bSrVajWzZs0iJSWFw4cPY7VauX79Ok1NTaSkpDh1+qjZbObSpUuOsz56e3tpaGjgxIkTGI1G1q5dy9q1a4mLi3voLUKdlozb/n/F9ObNmzGZTKxZs4bi4uK7Dq9wBg8PD7Zu3crOnTvvOI2up6eHtrY2rl+/zsjICL6+vo7jrt3c3MjMzHQM3wQGBjJt2jSUSiU+Pj4PvOrYbDbT3t7OlStXiIiIICUlxWnzLL9Np9PxzDPPEBQUxKeffkp7e/uE/6+kpIQVK1aQmJiIl5eX0zaj/3fMZjM2mw13d3dJkobR0VG+/PJLzGazY66eq05NtBsaGnJsleXm5kZtbS0HDx4kJCSEiooKlx8LrFarWbJkCXV1dRw7doxjx46RnZ39wIdoTGTatGmUl5dz48YNLly4QH9/P1qtltTUVFauXMnjjz/utN5TGNsD+YUXXuDy5ctYLBZCQkJ4+umnyczMlGTOn71n5dixYxw/fvyu5MBsNmM0GrFYLGg0GlasWMH69esfui1TqVRER0ezbt06Zs6cSWtrKxcvXqS9vZ2cnBzH/8vLyyMjI4P4+HjH6aRSOX36tOPUU41G4/T7rVQqiYmJoaysjOPHj7N7925UKhWrVq0iICCAmzdv0tXVRUNDA3/+85+5desWy5cvd+oaIxjrJGpububcuXPA2Hqa8vJyST5U9vb2curUKfr6+jhy5Ah6vZ7R0VF6enrw8fHh6NGjjIyM0Nvbi9VqRaVSodPpKCkpITc3l9jYWKf/Hrq6uuju7mZkZISkpCQiIiIka0PtQ/gnT55kYGCA6dOns3TpUp588kmnd8zB2DMWGRlJRUUFc+fOpaGhgS+++IKenh7HqPX4a4Ox3YUMBgNLlixh0aJFTjsrwh7j9OnTjp7r8aPXzmI/VfQ3v/kNt2/fxs3NjYULF/7H+/g/DKVSybx583jttdf461//SnNzM1OnTiU1NZWcnBwKCgqIi4uT5Hdtl5qaygsvvACMTVWxWq2EhIQ4LQez2Wy0tLRw4MABTp8+zfDwMF1dXZhMJvr6+tDpdGzZsoW8vDyioqKcUl+dlq2Njo5y6NAhampqsNlsLFmyhIiICEkSQqVSSXp6OgqF4o5ejv7+fsdm9DC2FVtUVJRju7CYmBinngw5ODiIVqslNzfXqSdBfptarSY7O5uIiAgSExOpra2lra2Nrq4uEhISyMjIwGq1UlxcTFZWlssT1RMnTmAymUhOTpZsdMDf3x+lUolWq3V5r/h4RqORuro6zp075/i9uDoRt5sxYwZr1qzh2rVrfP7557S2tjpljYBKpSIzM5OKigpqamo4e/asY9vO1NRUp8+vPHv2LAcPHmRkZASAJUuW8Oijjzo14R9v+fLl+Pn58eabb9LR0XHHQTwwNn8+IyODsLAwcnNzSU1NdVpvi5ubGwkJCUyfPp2MjAyuX79OZ2fnHUP3BoPBqcnB/XR3d3P79m0CAwMxGAySnEjq6elJVlYWmzdvZs+ePezdu5dr166RkZHBpUuXaGlp4ezZs3R0dJCYmEhxcbHTX+T2013tJ68uWrSIvLy8u7aIe1gxMTEsX74cf39/+vv7HesvhoaGcHd3x9/fn4GBAby9vZk/fz4KhYLo6Giio6MxGAwPtMXeg7AfAmc/H0HKbWE7OzvZtWsX3d3djsWiCxYsICIiQrK2UqlUkpiYSGBgIMnJyVy6dImrV686TjCOioqiq6uL6dOnExYWRnx8PNHR0cTHxzt9S2SbzebY0k+KURj72rd9+/Zx9OhRYGxRalhYmMve/RqNhvz8fIKCgnjiiSfw8PBAp9MRHR1NVFSU5JtpaLVaFixYgEajobu7m6CgIIKDg502St3Y2Mju3bupqanh+vXrqNVqzGYzYWFhFBUVkZOTw8KFC516YrVTar3VauXKlStUVlY6PomGhoZK/mBItaDpP6FSqcjJySEiIoLU1FTJ97zWarVotVrCw8OJj4/ns88+o7OzE4PBwNy5c7FarS655xPp6+sjMTGRpUuXSrLPulKpJD8/n87OTvR6vctOjBtPp9ORnJxMW1sbU6ZMISQkhPnz5zsl+f2u1Go1ixcvpqGhgY8++oiGhgays7Odksh4enrygx/8AJ1Ox1dffUVWVhYhISFOf75sNhtVVVWOdiMhIYFly5ZJOu8xJiaG6dOnMzg4OGEyrtFoSEtLIzw8XJJF2SqViqCgIJcdQ34/X3zxBTabjZSUFGbNmiVZ3dJoNJSWluLu7s4///lPGhsbOXXqFMPDw9y4ccOxh/78+fMlaUP8/Px47LHHSEtLc/wtBZ1Ox1NPPUViYiK9vb3cvHmTCxcuOD7weHt7ExwcjLe3N0lJSahUKvz9/SUfATlz5gy3b99Gq9Uya9YspyYR45nNZv7yl7/wySefOM5JyM7OJjk52SWdFvZ6lZmZidFoZGBggM8//5yYmBguX75McHAw4eHh+Pn5STZNxz6/ecaMGQ89j3giQ0NDHD9+nKqqKsc0uuLiYkna5/vRarUsWrToOy82flgBAQGSvX+vXr1KV1cXMNbppdfriYyMJCYmhry8PKee/G3nlKdkZGSElpYWamtrgbHdN/7daZH/7ZRKJcXFxS6Pq9VqmTdvnmxbvU0kNzeXyMhI5syZI0lvpkKhYNmyZVgsFvz8/CTrMb2fpKQkfvKTn9DS0kJSUhKPPfaYU46Gf1je3t5kZ2fT3t7OuXPn6O7uduqCx9mzZ0v6oddms/Huu+9is9nw8PCgpKSEOXPmSDrECWPJ4UsvvSRpjP8GJ0+exGq1kpqaKvmHXI1Gw7PPPkteXh5///vfaW5uJi4ujtjYWMfJheHh4ZL0qoWEhLBu3Tqn/9yJ6HQ6l2+7+u988sknDA8PM3PmTHx8fCSL093dzR//+Edu3ryJt7c3OTk55Obmuvx+uLm54evri6+vr2Pxs6sW3SsUCpYuXUp4eDjJyclOn3JkNBppbm7m4sWLREdHk5iYyKuvvirpyMNk88gjj/DDH/6Q5ORkQkJCWLBgAQkJCZK+753yk81ms2NejVqtJiMjA61WKx6MSeKnP/2p5DG0Wq2sW535+/tTWFhIYWGhbNdwL0VFRQwMDNDW1obJZJL7ch6YvYGLjIykuLjYZVM0hLGRiPb2dqZMmeKS9trT05PY2Fg2bNjAhg0bJI8njPn666+JiIggPT1d0vp16tQpBgcHUavVZGZm8rOf/YzMzMxJlQvYd3h58sknJfn5KpWKgIAAUlJSeOaZZ1i5cqVkIx2TlcFgeODd9R6WU5Jx+yrXadOmYTAYePfdd7/T7iSCIDw4tVrNc889J/dlfCcKhYLnn3+e1157jbKyMqKiomQfbZhMFi5cyOXLl0lOTpZsAbogP/u+zFLT6/VER0cTEBDAli1bmD17tqxrfP4X+fv7U1ZWRllZmdyXIjiRor+//5475EuxmEcQBEEQBEEQJhuLxTLhCPZ9u6CsVquke7IKgiAIgiAIwmRwrzUx903Gb926JcnFCIIgCIIgCIIA0m4GKQiCIAiCIAjCPYlkXBAEQRAEQRBkIpJxQRAEQRAEQZCJSMYFQRAEQRAEQSYiGRcEQRAEQRAEmYhkXBAEQRAEQRBk4vRkXKfT3fFHq9WyceNGZ4e5y8jICGVlZRgMBsLCwsjJyaG2tlbyuABvvfUW8+fPJzAw0CVHw9v19fXx9NNPExoaisFgYP/+/S6LbdfR0UFQUBDr1q2TPJZc93k8V5YX4Ny5cxQVFREREUFKSgrvv/++S+ICFBQUEBQU5KjLs2fPdklcucosZ32qqqpizpw5hIaGkpycTFNTk+Qx5Xy25LrXcrUhk60uyZUHAKxbt474+HjCw8NJS0tj9+7dLolrN5neESBP2yVXXCnrsdPPnb569arj3yaTCb1eT3FxsbPD3MVisaDT6fjggw8IDw/n448/prS0lMbGRiIjIyWNHRwcTHl5OYcPH2ZoaEjSWOOVl5fj4eHB+fPnOXHiBCtXrsRgMJCQkODSa0hNTXVJLLnu83iuLK/FYmH16tWUlpZy4MABGhoaKCkpISEhgdjYWJdcw7Zt2/jRj37kklggb5nlqk9Hjhxhy5YtvP3226SlpdHT0yNpPJD/2ZLrXsvZhkymuiRXHgDw8ssvs2PHDtRqNefPn6ewsJCkpCSSk5NdEn8yvSPkaLvkjAvS1WNJp6m89957BAQEMHfuXCnDAODl5UVFRQWRkZEolUry8/OJiIigra1N8thPPPEEhYWFaLVayWPZmUwmDh48yC9+8Qu8vb3JysoiPz+fvXv3uuwaqqqqmDZtGvPmzXNJPDnu83iuLu/58+fp6elh/fr1qFQqcnNzycjIoLKy0iXx5SBXmeWsT2+88QabNm0iPT0dpVJJaGgooaGhksaU89mS817L3Ya40vel/XBlHgCQkJCAWq0GQKFQoFAo+Oqrr1wSe7K9I+Rou+SMKyVJk/E9e/awatUqFAqFlGEm1NvbS0dHh0t7iV3pyy+/RKVS3fHpNzExkTNnzrgk/sDAAFu3buX11193STy5yVFem8024fdc9TsG+OUvf0l0dDSLFy+mvr5e8nhylVmu+jQ6Okprayv/+te/SElJYdasWWzcuFHyXls5ny252y65TJa69G1y5AGvvPIKISEhpKenExQUxOOPPy55zMn2jpCr7ZIrrp1U9ViyZLyrq4vGxkZKSkqkCnFPZrOZtWvXUlJSgl6vd3l8VzCZTGg0mju+p9FoMBqNLon/61//mjVr1hAWFuaSeHKTo7x6vZ6AgAB+//vfYzabOXz4MI2NjS5tdNra2jhz5gw//vGPKSkpkbyHSa4yy1Wfent7MZvNvPfee1RXV1NfX097ezu/+93vJI0r57Mld9slh8lUl8aTKw/Yvn07V65cobq6mqKiIkdPuZQm2ztCrrZLrrggbT2WLBmvrKwkMzOTGTNmSBViQlarleeffx4PDw+2bdvm0tiu5OXlxa1bt+743sDAAN7e3pLHbm9v5x//+Acvvvii5LG+D+Qqr7u7O++88w4fffQRer2eP/zhDyxbtsxlw3GzZ8/Gx8cHtVrN6tWrycjI4OOPP5Y0plxllqs+TZ06FRhbdBYcHIy/vz8vvvji/+x9BnnbLrlMpro0nlx5AIBKpSIrK4vu7m527dolaazJ+I6Qq+2SKy5IW4+dvoDTrrKykg0bNkj14ydks9koKyujt7eX/fv34+7u7tL4rhQbG4vFYqGjo4OYmBgATp486ZJpOQ0NDXR2dmIwGICxnq7R0VHOnj3Lp59+Knl8V5OzvAaDgQ8//NDxdV5eniyjTTA2/3KiYVFnk6PMctUnX19fdDqdLFP55Hq25Gy7vi/+l+vSeHLkAd9msVgkH4WYjO8IudouOdvMb3NmPZakZ/zYsWN8/fXXLls9bffzn/+c8+fPU1lZ6fj05AoWi4Xh4WFGR0cZHR1leHgYi8UiaUwvLy+KiorYunUrJpOJo0ePUl1dzcqVKyWNC/Dss8/S2tpKfX099fX1lJaWkpeXx9/+9jdJ48pxn0G+8sJYkjI8PMzg4CA7duygp6eH1atXSx63v7+fQ4cOOe7xvn37aGpqYtGiRZLHlqPMctan1atX89Zbb/HNN9/Q39/Pn/70JxYvXix5XLmeLTnvtRxtyGSrS3Zy5AHffPMNVVVVGI1GRkdHOXToEFVVVZIvqJyM7wiQr+2SI67U9ViSnvE9e/ZQWFiIj4+PFD9+Qp2dnbz99tuo1Wri4+Md33/zzTd56qmnJI29bds2fvvb3zq+3rdvH5s3b6aiokLSuNu3b2f9+vXExcWh1WrZvn27S3qXPD098fT0dHzt5eXFlClTCAgIkDSuXPdZrvIC7N27l927d2OxWMjKyuLAgQMumf9osVh4/fXXuXDhAkqlEr1ezzvvvENcXJzkseUqs1z1adOmTdy4cYO0tDSmTJlCcXEx5eXlkseV6z6DfPdajjZkMtYlkCcPUCgU7Nq1i5dffhmbzUZ4eDhvvPEGBQUFksadjO8IkK/tkiOu1PVY0d/fL/1YmSAIgiAIgiAId5F0a0NBEARBEARBEO5NJOOCIAiCIAiCIBORjAuCIAiCIAiCTEQyLgiCIAiCIAgyEcm4IAiCIAiCIMhEJOOCIAiCIAiCIBORjAuCIAiCIAiCTEQyLgiCIAiCIAgyEcm4IAiCIAiCIMjk/wCYXPOtGycEVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 24\n",
    "(training_digits, training_labels,\n",
    " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
    "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
    "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
    "font_digits, font_labels = create_digits_from_local_fonts(N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KIc0oqiD40HC"
   },
   "source": [
    "### Keras model: 3 convolutional layers, 2 dense layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 672
    },
    "colab_type": "code",
    "id": "56y8UNFQIVwj",
    "outputId": "9881b784-7da6-406c-8756-e1b9b71ec5c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 28, 28, 6)         54        \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 28, 28, 6)         18        \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 28, 28, 6)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 12)        2592      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 14, 14, 12)        36        \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 14, 14, 12)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 24)          10368     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 7, 7, 24)          72        \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 7, 7, 24)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1176)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 200)               235200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 200)               600       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                2010      \n",
      "=================================================================\n",
      "Total params: 250,950\n",
      "Trainable params: 250,466\n",
      "Non-trainable params: 484\n",
      "_________________________________________________________________\n",
      "Tensorboard loggs written to:  gs://ml1-demo-martin/mnist/mnist-logs/2020-06-23-23-46-45\n"
     ]
    }
   ],
   "source": [
    "# This model trains to 99.4%— sometimes 99.5%— accuracy in 10 epochs (with a batch size of 64)\n",
    "\n",
    "def make_model():\n",
    "    \n",
    "    model = tf.keras.Sequential(\n",
    "      [\n",
    "        tf.keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),\n",
    "\n",
    "        tf.keras.layers.Conv2D(filters=6, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm\n",
    "        tf.keras.layers.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before \"relu\"\n",
    "        tf.keras.layers.Activation('relu'), # activation after batch norm\n",
    "\n",
    "        tf.keras.layers.Conv2D(filters=12, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
    "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
    "        tf.keras.layers.Activation('relu'),\n",
    "\n",
    "        tf.keras.layers.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
    "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
    "        tf.keras.layers.Activation('relu'),\n",
    "\n",
    "        tf.keras.layers.Flatten(),\n",
    "          \n",
    "        tf.keras.layers.Dense(200, use_bias=False),\n",
    "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
    "        tf.keras.layers.Activation('relu'),\n",
    "          \n",
    "        tf.keras.layers.Dropout(0.5), # Dropout on dense layer only\n",
    "        tf.keras.layers.Dense(10, activation='softmax')\n",
    "      ])\n",
    "\n",
    "    model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "    \n",
    "with strategy.scope(): # the new way of handling distribution strategies in Tensorflow 1.14+\n",
    "    model = make_model()\n",
    "\n",
    "# print model layers\n",
    "model.summary()\n",
    "                        \n",
    "# set up learning rate decay\n",
    "lr_decay = tf.keras.callbacks.LearningRateScheduler(lambda epoch: learning_rate * math.pow(0.5, 1+epoch) + learning_rate/200, verbose=True)\n",
    "\n",
    "# set up Tensorboard logs\n",
    "timestamp = time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "log_dir=os.path.join(BUCKET, 'mnist-logs', timestamp)\n",
    "tb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, update_freq=50*global_batch_size)\n",
    "print(\"Tensorboard loggs written to: \", log_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CuhDh8ao8VyB"
   },
   "source": [
    "### Train and validate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1162
    },
    "colab_type": "code",
    "id": "TTwH_P-ZJ_xx",
    "outputId": "a5be8502-8a51-4f68-80c7-ec2d586d200b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step (batches) per epoch:  117\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 0.00808.\n",
      "Epoch 1/10\n",
      "  2/117 [..............................] - ETA: 2:33 - accuracy: 0.3652 - loss: 2.0532WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.480141). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.480141). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  3/117 [..............................] - ETA: 1:51 - accuracy: 0.5026 - loss: 1.6069WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.304145). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.304145). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.157504). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.157504). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 8s 66ms/step - accuracy: 0.9394 - loss: 0.2027 - val_accuracy: 0.9049 - val_loss: 0.2827 - lr: 0.0081\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 0.00408.\n",
      "Epoch 2/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9746 - loss: 0.0785WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.257462). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.257462). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.134284). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.134284). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 25ms/step - accuracy: 0.9805 - loss: 0.0649 - val_accuracy: 0.9300 - val_loss: 0.1997 - lr: 0.0041\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0020800000000000003.\n",
      "Epoch 3/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9805 - loss: 0.0725WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.242580). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.242580). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.126361). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.126361). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 26ms/step - accuracy: 0.9865 - loss: 0.0452 - val_accuracy: 0.9813 - val_loss: 0.0663 - lr: 0.0021\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 0.00108.\n",
      "Epoch 4/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9883 - loss: 0.0391WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.280624). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.280624). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.145483). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.145483). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 25ms/step - accuracy: 0.9898 - loss: 0.0345 - val_accuracy: 0.9902 - val_loss: 0.0293 - lr: 0.0011\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 0.00058.\n",
      "Epoch 5/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 1.0000 - loss: 0.0158WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.299909). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.299909). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.155012). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.155012). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 25ms/step - accuracy: 0.9913 - loss: 0.0296 - val_accuracy: 0.9913 - val_loss: 0.0256 - lr: 5.8000e-04\n",
      "\n",
      "Epoch 00006: LearningRateScheduler reducing learning rate to 0.00033.\n",
      "Epoch 6/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9941 - loss: 0.0163WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.226687). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.226687). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.118889). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.118889). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 26ms/step - accuracy: 0.9924 - loss: 0.0260 - val_accuracy: 0.9920 - val_loss: 0.0239 - lr: 3.3000e-04\n",
      "\n",
      "Epoch 00007: LearningRateScheduler reducing learning rate to 0.000205.\n",
      "Epoch 7/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 1.0000 - loss: 0.0088WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.216980). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.216980). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.113795). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.113795). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 26ms/step - accuracy: 0.9931 - loss: 0.0234 - val_accuracy: 0.9920 - val_loss: 0.0241 - lr: 2.0500e-04\n",
      "\n",
      "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0001425.\n",
      "Epoch 8/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9922 - loss: 0.0230WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.234824). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.234824). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.122462). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.122462). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 24ms/step - accuracy: 0.9928 - loss: 0.0228 - val_accuracy: 0.9918 - val_loss: 0.0232 - lr: 1.4250e-04\n",
      "\n",
      "Epoch 00009: LearningRateScheduler reducing learning rate to 0.00011125000000000001.\n",
      "Epoch 9/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9941 - loss: 0.0190WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.251358). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.251358). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.131499). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.131499). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 26ms/step - accuracy: 0.9938 - loss: 0.0223 - val_accuracy: 0.9922 - val_loss: 0.0230 - lr: 1.1125e-04\n",
      "\n",
      "Epoch 00010: LearningRateScheduler reducing learning rate to 9.562500000000001e-05.\n",
      "Epoch 10/10\n",
      "  1/117 [..............................] - ETA: 0s - accuracy: 0.9961 - loss: 0.0154WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.249420). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.249420). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.129988). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.129988). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 3s 25ms/step - accuracy: 0.9940 - loss: 0.0215 - val_accuracy: 0.9922 - val_loss: 0.0226 - lr: 9.5625e-05\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "steps_per_epoch = 60000//global_batch_size  # 60,000 items in this dataset\n",
    "print(\"Step (batches) per epoch: \", steps_per_epoch)\n",
    "  \n",
    "history = model.fit(training_dataset, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
    "                            validation_data=validation_dataset, validation_steps=1, callbacks=[lr_decay, tb_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9jFVovcUUVs1"
   },
   "source": [
    "### Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 790
    },
    "colab_type": "code",
    "id": "w12OId8Mz7dF",
    "outputId": "6f5a05f0-dac5-4bea-a713-32e51f85fc79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recognize digits from local fonts\n",
    "probabilities = model.predict(font_digits, steps=1)\n",
    "predicted_labels = np.argmax(probabilities, axis=1)\n",
    "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
    "\n",
    "# recognize validation digits\n",
    "probabilities = model.predict(validation_digits, steps=1)\n",
    "predicted_labels = np.argmax(probabilities, axis=1)\n",
    "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SVY1pBg5ydH-"
   },
   "source": [
    "## License"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hleIN5-pcr0N"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "author: Martin Gorner<br>\n",
    "twitter: @martin_gorner\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "Copyright 2020 Google LLC\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License.\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "This is not an official Google product but sample code provided for an educational purpose\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Keras MNIST TPU (public).ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "environment": {
   "name": "tf22-cpu.2-2.m47",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf22-cpu.2-2:m47"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
